A 3D Sequential LiDAR Data Registration Method for Unmanned Ground Vehicle

2014 ◽  
Vol 664 ◽  
pp. 365-370
Author(s):  
Chao Chen ◽  
Yan Li ◽  
Wei Wang
Keyword(s):  
Gaussian Process ◽  
Point Cloud ◽  
Dynamic Feature ◽  
Unmanned Ground Vehicle ◽  
Lidar System ◽  
Registration Method ◽  
Ground Vehicle ◽  
Data Registration ◽  
Ground Segmentation ◽  
Fine Registration

This paper proposes a 3D point cloud registration method based on light detection and ranging (LiDAR) system. The proposed method consists of three steps: Gaussian-Process based ground segmentation, a novel k-neighbors based dynamic point feature and Iterative Closest Point (ICP) fine registration. The first two steps are the preparation of ICP fine registration. The odometry information from a GPS/IMU system is used to compensate the vehicle's ego-motion. The Gaussian-Process based ground segmentation is adopted to remove ground points. A novel Initial Localization based Dynamic Feature (ILDF) is proposed to detect and remove dynamic points. It is applicable in sequential frames and a proper initial localization without a large dislocation. In experiment results, a large number of dynamic points will be detected and removed by ILDF. The removal of dynamic points improves both accuracy and efficiency of registration algorithm.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

Study on the Data Registration Method of Airborne Bathymetric LiDAR System and Ship-Based Mobile Measurement System

2018 ◽  
Vol 55 (8) ◽  
pp. 082802
Author(s):  
田茂义 Tian Maoyi ◽  
王延存 Wang Yancun ◽  
俞家勇 Yu Jiayong ◽  
贺岩 He Yan ◽  
曹岳飞 Cao Yuefei ◽  
...  
Keyword(s):  
Measurement System ◽  
Lidar System ◽  
Registration Method ◽  
Data Registration ◽  
Mobile Measurement

scholarly journals Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

2020 ◽  
Vol 10 (8) ◽  
pp. 2808 ◽  
Author(s):  
Chao Yin ◽  
Haoran Li ◽  
Zhinan Hu ◽  
Ying Li
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Deformation Monitoring ◽  
Slope Deformation ◽  
Model Parameters ◽  
Feature Points ◽  
Registration Method ◽  
Icp Algorithm ◽  
Fine Registration

Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.

Measuring Data Registration Based on Triangle Constraint

2012 ◽  
Vol 271-272 ◽  
pp. 1346-1350
Author(s):  
Tie Li Ye ◽  
Xue Yi Li ◽  
Qing Liang Zeng
Keyword(s):  
Coordinate System ◽  
Reference Points ◽  
Measuring Error ◽  
Registration Method ◽  
Reference Coordinate System ◽  
Complex Product ◽  
Registration Error ◽  
Data Registration ◽  
Registration Algorithm ◽  
Fine Registration

This paper studied the fine registration techniques of measuring data of complex product and proposed a new registration method based on triangle constraint. Firstly, the paper made a research on specifying the proper measuring reference points and the setting of reference coordinate system, and afterwards proposed a fine registration algorithm of measuring data, Which is about realizing the fusion of reference coordinate system based on the congruent judgment of reference triangle and fulfilling precise match of data with measuring error on the basis of controlling the rotation of data blocks and minimizing the registration error.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Design and Realization of an All-terrain Unmanned Ground Vehicle

ROBOT ◽  
2013 ◽  
Vol 35 (6) ◽  
pp. 657 ◽  
Author(s):  
Taoyi ZHANG ◽  
Tianmiao WANG ◽  
Yao WU ◽  
Qiteng ZHAO
Keyword(s):  
Unmanned Ground Vehicle ◽  
Ground Vehicle

Unmanned Ground Vehicle for Security and Surveillance

2020 ◽  
Author(s):  
Prajot P. Kulkarni ◽  
Shubham R. Kutre ◽  
Shravan S. Muchandi ◽  
Pournima Patil ◽  
Shankargoud Patil
Keyword(s):  
Unmanned Ground Vehicle ◽  
Ground Vehicle
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